To a scientist, observer effects corrupting empirical data is a constant concern. From psychology to quantum physics, observation of the world invariably changes what is observed.

The global empire Smartphones has created new avenues for data collection and some researchers believe this technology may help conquer observer effect uprisings.

Chad Tossel and his team studied information collection applications, known as ‘loggers’ for the purpose of research on Smartphones, and proposed a framework for future loggers that avoid observer effects on data (Tossel et al, 2012).

The problem Tossel and his team report is change in user behaviour due to awareness of the data collection process. Factors such as unfamiliar logger interfaces or automated participant reminders render collected information invalid.

The team provide nine factors to inform and refine future research that employs logging applications. They are:

1. Variables

2. Privacy

3. Obtrusiveness

4. Interface

5. Tasks

6. Technology

7. Participants

8. Setting

9. Study Duration

The researchers point out the relative importance of each of the nine factors will be determined by the aim of the particular research being conducted. They also acknowledge that unobtrusive logging is limited in some ways, such as being incapable of accessing experiential aspects of what logged data may denote. However, they maintain that refined and automated data logging techniques garnered by their framework have important contributions to make to the domain of behavioural research.